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I am trying to carry out diagnostics on the mixed effects logistic regression model below .
mod <- lmer(CEever ~ (1|SL)
+ birthWeightCat
+ AFno
+ FRAgeY*factor(genCat)
+ damGirBir
+ factor(YNSUPPLEM),
data=Data, family="binomial")
The data for this model is in the form:
head(data)
CalfID CEever birthWeightCat AFno FRAgeY damGirBir YNSUPPLEM
305 CA010110001 1 <20 2 48 140.0 1
306 CA010110002 1 21-25 1 45 144.0 0
307 CA010110004 0 21-25 1 47 151.5 0
308 CA010110005 0 <20 2 71 147.0 0
309 CA010110006 0 <20 1 57 141.5 1
310 CA010110007 0 <20 1 53 141.5 1
I can plot the residuals:
res <- resid(mod)
plot(res)
.... but can't get values for leverage or Cook's Distance and Dfbeta.
Firstly are these useful techniques for use with this model type, and then if so what code have people used to get these values.
332
Mixed-model formulas. Like most model-fitting functions in R, lmer takes as its first two arguments a formula spec- ifying the model and the data with which to evaluate the formula. This second argument, data, is optional but recommended and is usually the name of an R data frame.
lmerTest: Tests in Linear Mixed Effects Models A Kenward-Roger method is also available via the pbkrtest package. Model selection methods include step, drop1 and anova-like tables for random effects (ranova). Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed effects are also available.
Mixed effects logistic regression is used to model binary outcome variables, in which the log odds of the outcomes are modeled as a linear combination of the predictor variables when data are clustered or there are both fixed and random effects.
Have a look at the influence.ME package at CRAN.
157
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